Welcome to Anagrammer Crossword Genius! Keep reading below to see if regularize is an answer to any crossword puzzle or word game (Scrabble, Words With Friends etc). Scroll down to see all the info we have compiled on regularize.
regularize
Searching in Crosswords ...
The answer REGULARIZE has 0 possible clue(s) in existing crosswords.
Searching in Word Games ...
The word REGULARIZE is VALID in some board games. Check REGULARIZE in word games in Scrabble, Words With Friends, see scores, anagrams etc.
Searching in Dictionaries ...
Definitions of regularize in various dictionaries:
verb - bring into conformity with rules or principles or usage
verb - make regular or more regular
REGULARIZE - Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resu...
Word Research / Anagrams and more ...
Keep reading for additional results and analysis below.
Possible Dictionary Clues |
---|
To make regular cause to conform. |
make (something) regular. |
make regular or more regular |
bring into conformity with rules or principles or usage impose regulations |
to change a situation or system so that it obeys laws or is based on reason: |
to change a system or a situation so that it is controlled by a set of official rules: |
Regularize might refer to |
---|
Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting solution. * RLS is used for two main reasons. The first comes up when the number of variables in the linear system exceeds the number of observations. In such settings, the ordinary least-squares problem is ill-posed and is therefore impossible to fit because the associated optimization problem has infinitely many solutions. RLS allows the introduction of further constraints that uniquely determine the solution. * The second reason that RLS is used occurs when the number of variables does not exceed the number of observations, but the learned model suffers from poor generalization. RLS can be used in such cases to improve the generalizability of the model by constraining it at training time. This constraint can either force the solution to be "sparse" in some way or to reflect other prior knowledge about the problem such as information about correlations between features. A Bayesian understanding of this can be reached by showing that RLS methods are often equivalent to priors on the solution to the least-squares problem. |